2 papers
cs.CL2025
Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis
Felipe Ribeiro Fujita de Mello, Hideyuki Takada
We investigated the impact of data selection on machine translation fine-tuning for open LLMs. Using Japanese-English corpora, we compare five selectors: TF-IDF, COMET Kiwi, QuRate…
cs.CL2025
Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task
Felipe Fujita, Hideyuki Takada
In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English…